We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find the network needs to be trained on only a small sampling of the data in order to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used solve nanophotonic inverse design problems by using back-propogation - where the gradient is analytical, not numerical.

Date Published: 23 February 2018
PDF: 6 pages
Proc. SPIE 10526, Physics and Simulation of Optoelectronic Devices XXVI, 1052607 (23 February 2018); doi: 10.1117/12.2289195

Published in SPIE Proceedings Vol. 10526:
Physics and Simulation of Optoelectronic Devices XXVI
Bernd Witzigmann; Marek Osiński; Yasuhiko Arakawa, Editor(s)